Comparer des méthodes

	Fiabilité test-retest multiniveau	Modélisation multiniveau
Domaine	Psychométrie	Statistiques de recherche
Famille	Latent structure	Process / pipeline
Année d'origine	1979 (ICC foundation); multilevel extension: 1990s–2000s	1992
Auteur d'origine	Shrout & Fleiss (ICC foundation); multilevel extension by Goldstein, Snijders, and others	Anthony Bryk and Stephen Raudenbush
Type	Reliability estimation under hierarchical data	Method
Source fondatrice	Shrout, P. E. & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Alias	hierarchical test-retest reliability, multilevel ICC reliability, nested test-retest reliability, ML-TRT reliability	HLM, mixed-effects models, random effects models, MLM
Apparentées	5	3
Résumé	Multilevel test-retest reliability estimates how consistently a measurement instrument produces the same scores across repeated administrations when observations are nested within higher-level units — such as patients within clinics or students within classrooms. It partitions total score variance across levels using intraclass correlation coefficients derived from multilevel models.	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.